""":
Deep Learning Assignment 3
Conditional GAN Skeleton Code.
Adopted from public sources, customized and improved for this assignment.
"""
#import necessary modules
import torch
import torch.nn as nn
from torchvision import transforms, datasets
from torch import optim as optim
# for visualization
from matplotlib import pyplot as plt
import math
import numpy as np
%load_ext tensorboard
import tensorflow as tf
import datetime
# tells PyTorch to use an NVIDIA GPU, if one is available.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# loading the dataset
training_parameters = {
"img_size": 28,
"n_epochs": 24, #24
"batch_size": 64,
"learning_rate_generator": 0.0002,
"learning_rate_discriminator": 0.0002,
}
# define a transform to 1) scale the images and 2) convert them into tensors
transform = transforms.Compose([
transforms.Resize(training_parameters['img_size']), # scales the smaller edge of the image to have this size
transforms.ToTensor(),
])
# load the dataset
train_loader = torch.utils.data.DataLoader(
datasets.FashionMNIST(
'./data', # specifies the directory to download the datafiles to, relative to the location of the notebook.
train = True,
download = True,
transform = transform),
batch_size = training_parameters["batch_size"],
shuffle=True
)
# Fashion MNIST has 10 classes, just like MNIST. Here's what they correspond to:
label_descriptions = {
0: 'T-shirt/top',
1 : 'Trouser',
2 : 'Pullover',
3 : 'Dress',
4 : 'Coat',
5 : 'Sandal',
6 : 'Shirt',
7 : 'Sneaker',
8 : 'Bag',
9 : 'Ankle boot'
}
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz
100%|██████████| 26421880/26421880 [00:01<00:00, 15998023.58it/s]
Extracting ./data/FashionMNIST/raw/train-images-idx3-ubyte.gz to ./data/FashionMNIST/raw Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
100%|██████████| 29515/29515 [00:00<00:00, 267152.26it/s]
Extracting ./data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
100%|██████████| 4422102/4422102 [00:00<00:00, 5056578.21it/s]
Extracting ./data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST/raw Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
100%|██████████| 5148/5148 [00:00<00:00, 19611514.07it/s]
Extracting ./data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST/raw
# Create the Generator model class, which will be used to initialize the generator
class Generator(nn.Module):
def __init__(self, input_dim, output_dim, num_labels=10): # to initialize the model-wide parameters. When you run `generator = Generator(params)`, those "params" are passed to __init__.
super(Generator,self).__init__() # initialize the parent class
# TODO (5.4) Turn this Generator into a Conditional Generator by
# 1. Adjusting the input dimension of the first hidden layer.
# 2. Modifying the input to the first hidden layer in the forward class.
self.hidden_layer1 = nn.Sequential(
nn.Linear(input_dim, 256),
nn.LeakyReLU(0.2)
)
self.hidden_layer2 = nn.Sequential(
nn.Linear(256, 512),
nn.LeakyReLU(0.2)
)
self.hidden_layer3 = nn.Sequential(
nn.Linear(512, 1024),
nn.LeakyReLU(0.2)
)
self.hidden_layer4 = nn.Sequential(
nn.Linear(1024, output_dim),
nn.Tanh()
)
def forward(self, x, labels):
output = self.hidden_layer1(x)
output = self.hidden_layer2(output)
output = self.hidden_layer3(output)
output = self.hidden_layer4(output)
return output.to(device)
class Discriminator(nn.Module):
def __init__(self, input_dim, output_dim=1, num_labels=None):
super(Discriminator, self).__init__()
#self.label_embedding = nn.Embedding(10, 10)
# TODO (5.4) Modify this discriminator to function as a conditional discriminator.
self.hidden_layer1 = nn.Sequential(
nn.Linear(input_dim, 1024),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden_layer2 = nn.Sequential(
nn.Linear(1024, 512),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden_layer3 = nn.Sequential(
nn.Linear(512, 256),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden_layer4 = nn.Sequential(
nn.Linear(256, output_dim),
nn.Sigmoid()
)
def forward(self, x, labels=None): # labels to be used in 5.4.
output = self.hidden_layer1(x)
output = self.hidden_layer2(output)
output = self.hidden_layer3(output)
output = self.hidden_layer4(output)
return output.to(device)
discriminator = Discriminator(784,1).to(device) # initialize both models, and load them to the GPU or CPU.
generator = Generator(100,784).to(device)
discriminator_optimizer = optim.Adam(discriminator.parameters(), lr=training_parameters['learning_rate_discriminator'])
generator_optimizer = optim.Adam(generator.parameters(), lr=training_parameters['learning_rate_generator'])
# Establish convention for real and fake labels during training
real_label = 1.
fake_label = 0.
#Loss_D - discriminator loss calculated as the sum of losses for the all real and all fake batches $(\log (D(x))+\log (1- D(G(z))))
loss_func = nn.BCELoss() # Binary Cross Entropy Loss
def train_generator(batch_size):
"""
Performs a training step on the generator by
1. Generating fake images from random noise.
2. Running the discriminator on the fake images.
3. Computing loss on the result.
:arg batch_size: the number of training examples in the current batch
Returns the average generator loss over the batch.
"""
# Start by zeroing the gradients of the optimizer
generator_optimizer.zero_grad()
# 1. Create a new batch of fake images (since the discriminator has just been trained on the old ones)
noise = torch.randn(batch_size,100).to(device) # whenever you create new variables for the model to process, send them to the device, like this.
generated_labels = torch.randint(0, 10, (batch_size,)).to(device)
generator_output = generator(noise, labels = generated_labels)
# 2. Run the discriminator on the fake images
discriminator_output = discriminator(generator_output, labels = generated_labels)
###----copied----
real_label_vector = torch.full((batch_size,), real_label, dtype=torch.float, device=device)
real_label_vector = real_label_vector.view(-1, 1)
#-------
# 3. Compute the loss
loss = loss_func(discriminator_output, real_label_vector)
loss.backward()
generator_optimizer.step()
loss = loss.mean().item()
return loss
def train_discriminator(batch_size, images, labels=None): # labels to be used in 5.4.
"""
Performs a training step on the discriminator by
1. Generating fake images from random noise.
2. Running the discriminator on the fake images.
3. Running the discriminator on the real images
3. Computing loss on the results.
:arg batch_size: the number of training examples in the current batch
:arg images: the current batch of images, a tensor of size BATCH x 1 x 64 x 64
:arg labels: the labels corresponding to images, a tensor of size BATCH
Returns the average loss over the batch.
"""
discriminator_optimizer.zero_grad()
###----fake images----###
# 1. Create a new batch of fake images (since the discriminator has just been trained on the old ones)
noise = torch.randn(batch_size,100).to(device) # whenever you create new variables for the model to process, send them to the device, like this.
generated_labels = torch.randint(0, 10, (batch_size,)).to(device)
generator_output = generator(noise, labels = generated_labels)
# 2. Run the discriminator on the fake images
discriminator_output = discriminator(generator_output, labels = generated_labels)
# 3. Compute the loss
fake_label_vector = torch.full((batch_size,), fake_label, dtype=torch.float, device=device)
fake_label_vector = fake_label_vector.view(-1, 1)
loss_fake = loss_func(discriminator_output, fake_label_vector)
###----real images----###
# 1. Run the discriminator on the real images
images = torch.flatten(images, start_dim=1)
discriminator_output = discriminator(images, labels = labels)
# 2. Compute the loss
real_label_vector = torch.full((batch_size,), real_label, dtype=torch.float, device=device)
real_label_vector = real_label_vector.view(-1, 1)
loss_real = loss_func(discriminator_output, real_label_vector)
#combine losses
loss = loss_real + loss_fake
loss.backward()
discriminator_optimizer.step()
loss = loss.mean().item()
return loss
for epoch in range(training_parameters['n_epochs']):
G_loss = [] # for plotting the losses over time
D_loss = []
for batch, (imgs, labels) in enumerate(train_loader):
batch_size = labels.shape[0] # if the batch size doesn't evenly divide the dataset length, this may change on the last epoch.
#generator first training
lossG = train_generator(batch_size)
G_loss.append(lossG)
#single discriminator training
lossD = train_discriminator(batch_size, imgs, labels)
D_loss.append(lossD)
if ((batch + 1) % 500 == 0 and (epoch + 1) % 1 == 0):
# Display a batch of generated images and print the loss
print("Training Steps Completed: ", batch)
with torch.no_grad(): # disables gradient computation to speed things up
noise = torch.randn(batch_size, 100).to(device)
fake_labels = torch.randint(0, 10, (batch_size,)).to(device)
generated_data = generator(noise, fake_labels).cpu().view(batch_size, 28, 28)
# display generated images
batch_sqrt = int(training_parameters['batch_size'] ** 0.5)
fig, ax = plt.subplots(batch_sqrt, batch_sqrt, figsize=(15, 15))
for i, x in enumerate(generated_data):
#ax[math.floor(i / batch_sqrt)][i % batch_sqrt].set_title(label_descriptions[int(fake_labels[i].item())])
ax[math.floor(i / batch_sqrt)][i % batch_sqrt].imshow(x.detach().numpy(), interpolation='nearest', cmap='gray')
ax[math.floor(i / batch_sqrt)][i % batch_sqrt].get_xaxis().set_visible(False)
ax[math.floor(i / batch_sqrt)][i % batch_sqrt].get_yaxis().set_visible(False)
plt.show()
#fig.savefig(f"./results/CGAN_Generations_Epoch_{epoch}")
#fig.savefig(f"pset/pset3/results/CGAN_Generations_Epoch_{epoch}")
fig.savefig(f"CGAN_Generations_Epoch_{epoch}")
print(
f"Epoch {epoch}: loss_d: {torch.mean(torch.FloatTensor(D_loss))}, loss_g: {torch.mean(torch.FloatTensor(G_loss))}")
Training Steps Completed: 499
Epoch 0: loss_d: 1.225581407546997, loss_g: 1.4263771772384644 Training Steps Completed: 499
Epoch 1: loss_d: 0.9694660305976868, loss_g: 1.8247395753860474 Training Steps Completed: 499
Epoch 2: loss_d: 1.1399441957473755, loss_g: 1.3783767223358154 Training Steps Completed: 499
Epoch 3: loss_d: 0.9844392538070679, loss_g: 1.3892009258270264 Training Steps Completed: 499
Epoch 4: loss_d: 0.9357741475105286, loss_g: 1.586155652999878 Training Steps Completed: 499
Epoch 5: loss_d: 0.9688796997070312, loss_g: 1.4648841619491577 Training Steps Completed: 499
Epoch 6: loss_d: 0.9627387523651123, loss_g: 1.4375097751617432 Training Steps Completed: 499
Epoch 7: loss_d: 0.9848072528839111, loss_g: 1.3207499980926514 Training Steps Completed: 499
Epoch 8: loss_d: 0.9613051414489746, loss_g: 1.4039595127105713 Training Steps Completed: 499
Epoch 9: loss_d: 0.9866572618484497, loss_g: 1.3418234586715698 Training Steps Completed: 499
Epoch 10: loss_d: 0.9602514505386353, loss_g: 1.6135871410369873 Training Steps Completed: 499
Epoch 11: loss_d: 0.9789851903915405, loss_g: 1.3583877086639404 Training Steps Completed: 499
Epoch 12: loss_d: 0.9949526786804199, loss_g: 1.2763011455535889 Training Steps Completed: 499
Epoch 13: loss_d: 1.007961392402649, loss_g: 1.3118679523468018 Training Steps Completed: 499
Epoch 14: loss_d: 0.9944226145744324, loss_g: 1.4150930643081665 Training Steps Completed: 499
Epoch 15: loss_d: 0.9656962752342224, loss_g: 1.4043164253234863 Training Steps Completed: 499
Epoch 16: loss_d: 0.9760198593139648, loss_g: 1.3481272459030151 Training Steps Completed: 499
Epoch 17: loss_d: 0.9081671833992004, loss_g: 1.4236488342285156 Training Steps Completed: 499
Epoch 18: loss_d: 0.9437633156776428, loss_g: 1.291171908378601 Training Steps Completed: 499
Epoch 19: loss_d: 0.9344487190246582, loss_g: 1.3899822235107422 Training Steps Completed: 499
Epoch 20: loss_d: 0.9814656376838684, loss_g: 1.3209909200668335 Training Steps Completed: 499
Epoch 21: loss_d: 0.9480006098747253, loss_g: 1.3294214010238647 Training Steps Completed: 499
Epoch 22: loss_d: 0.9748787879943848, loss_g: 1.3365697860717773 Training Steps Completed: 499
Epoch 23: loss_d: 0.9892889857292175, loss_g: 1.280814528465271
#save the model
torch.save(generator.state_dict(), 'generator_cond.pth')
torch.save(discriminator.state_dict(), 'discriminator_cond.pth')